Reliability pattern classification system and method

Abstract

A reliability pattern classification system includes a communication device configured to obtain historical data indicative of usage of a component of a powered system, and a control unit that can create a visual representation of the historical data. The control unit also can identify one or more reliability patterns within the visual representation using a vision-based, deep learning model, categorize a failure mode of the component based on the one or more reliability patterns that are identified, and implement one or more responsive actions to change a state of condition of the component, the powered system, or both the component and the powered system.

What is claimed is: 
     
         1 . A reliability pattern classification system comprising:
 a communication device configured to obtain historical data indicative of usage of a component of a powered system; and   a control unit configured to create a visual representation of the historical data, identify one or more reliability patterns within the visual representation using a vision-based, deep learning model, categorize a failure mode of the component based on the one or more reliability patterns that are identified, and implement one or more responsive actions to change a state of condition of the component, the powered system, or both the component and the powered system.   
     
     
         2 . The reliability pattern classification system of  claim 1 , wherein the communication device is configured to obtain raw data as the historical data and the control unit is configured to create the visual representation from the raw data. 
     
     
         3 . The reliability pattern classification system of  claim 2 , wherein the raw data has not been changed, formatted, altered, cleaned, sorted, converted, or structured following creation of the raw data. 
     
     
         4 . The reliability pattern classification system of  claim 1 , wherein the control unit is configured to identify the one or more reliability patterns using visual inspection of the visual representation of the historical data. 
     
     
         5 . The reliability pattern classification system of  claim 1 , wherein the historical data includes maintenance information about the component. 
     
     
         6 . The reliability pattern classification system of  claim 1 , wherein the control unit is configured to use the vision-based, deep learning model that was trained using one or more of synthetic data, or human-labeled data to identify the one or more reliability patterns. 
     
     
         7 . The reliability pattern classification system of  claim 1 , wherein the control unit is an artificial neural network trained using a pre-trained model for identifying the patterns in the visual representations. 
     
     
         8 . The reliability pattern classification system of  claim 1 , wherein the control unit is configured to implement the one or more responsive actions based on the failure mode that is categorized. 
     
     
         9 . The reliability pattern classification system of  claim 1 , wherein the one or more responsive actions include one or more of replacing the component, repairing the component, identifying a product design flaw in the component, changing a maintenance process associated with the powered system or the component, identifying and avoiding further supply from a supplier of the component, identifying the component as an anomaly, or changing a priority of manual inspection of the component relative to one or more other components. 
     
     
         10 . A method comprising:
 obtaining historical data indicative of usage of a component of a powered system;   creating a visual representation of the historical data;   identifying one or more reliability patterns within the visual representation using a vision-based, deep learning model;   categorizing a failure mode of the component based on the one or more reliability patterns that are identified; and   implementing one or more responsive actions to change a state of condition of the component, the powered system, or both the component and the powered system.   
     
     
         11 . The method of  claim 10 , wherein the historical data that is obtained is raw data and the visual representation is created from the raw data. 
     
     
         12 . The method of  claim 10 , wherein the one or more reliability patterns are identified using visual inspection of the visual representation of the historical data. 
     
     
         13 . The method of  claim 10 , wherein the historical data includes maintenance information about the component. 
     
     
         14 . The method of  claim 10 , wherein the one or more reliability patterns are identified using the vision-based, deep learning model that was trained using one or more of synthetic data or human-labeled data. 
     
     
         15 . The method of  claim 10 , wherein identifying the one or more reliability patterns and categorizing the failure mode is performed using an artificial neural network that is trained using a pre-trained model for identifying the patterns in the visual representations. 
     
     
         16 . The method of  claim 10 , wherein the one or more responsive actions that are implemented is based on the failure mode that is categorized. 
     
     
         17 . The method of  claim 10 , wherein the one or more responsive actions include one or more of replacing the component, repairing the component, identifying a product design flaw in the component, changing a maintenance process associated with the powered system or the component, identifying and avoiding further supply from a supplier of the component, identifying the component as an anomaly, or changing a priority of manual inspection of the component relative to one or more other components. 
     
     
         18 . A method comprising:
 creating visual representations of raw maintenance data of components of an aircraft;   visually identifying patterns within the visual representations using a vision-based, deep learning model;   categorizing the components into different failure modes based on the patterns that are visually identified; and   changing a state of the aircraft based on at least one of the failure modes into which at least one of the components is categorized.   
     
     
         19 . The method of  claim 18 , wherein visually identifying the patterns and categorizing the components is performed using an artificial neural network that is trained using a pre-trained model for identifying the patterns within the visual representations. 
     
     
         20 . The method of  claim 18 , wherein the raw maintenance data includes one or more of flight hours of the components, flight cycles of the components, or days on wing of the components without altering the raw maintenance data.